Key Issues and Application Analysis of Multimodal Emotion Recognition

This article mainly explores the key issues of multimodal emotion recognition and analyzes some applications. It discusses the key issues from two core problems: data quality and coverage, semantic understanding and modal association. In the issue of data quality coverage, it is pointed out whether the data is good enough and comprehensive enough. In semantic understanding and modal association, it is pointed out whether the semantics are clear and whether they can be coordinated with other modalities. It also combines two core issues to discover the problems that occur in multimodal scenarios. At the application level, research on learning emotion recognition for the education field is listed. By real-time judgment of learning concentration and emotional state, it provides support for personalized teaching. The design and application of multimodal emotion recognition technology in security systems, the role of multimodal emotion recognition in security, MemoCMT: Multimodal emotion recognition is achieved through feature fusion based on cross-modal converters. By using an innovative feature fusion strategy, the three application aspects of multimodal emotion features are effectively integrated.